quantax.optimizer.TDVP

class quantax.optimizer.TDVP

Bases: QNGD

Time-dependent variational principle TDVP, or stochastic reconfiguration (SR).

__init__(state: Variational, hamiltonian: Operator, imag_time: bool = True, solver: Callable | None = None, kazcmarz_mu: float = 0.0)

Parameters:

• state – Variational state to be optimized.

• hamiltonian – The Hamiltonian for the evolution.

• imag_time – Whether to use imaginary-time evolution, default to True.

• solver – The numerical solver for the matrix inverse, default to auto_pinv_eig.

• use_kazcmarz – Whether to use the kazcmarz scheme, default to False.

property hamiltonian: Operator

The Hamiltonian for the evolution.

property energy: float | None

Energy of the current step.

property VarE: float | None

Energy variance \(\left< (H - E)^2 \right>\) of the current step.

get_Ebar(samples: Samples) → Array

Compute \(\bar \epsilon\) for given samples. The local energy is \(E_{loc, s} = \sum_{s'} \frac{\psi_{s'}}{\psi_s} \left< s|H|s' \right>\), and \(\bar \epsilon\) is defined as \(\bar \epsilon = \frac{1}{\sqrt{N_s}} (E_{loc, s} - \left<E_{loc, s}\right>)\).

get_Obar(samples: Samples) → Array

Calculate \(\bar O = \frac{1}{\sqrt{N_s}}(\frac{1}{\psi} \frac{\partial \psi}{\partial \theta} - \left< \frac{1}{\psi} \frac{\partial \psi}{\partial \theta} \right>)\) for given samples.

get_step(samples: Samples) → Array

Obtain the optimization step by solving the equation \(\bar O \dot \theta = \bar \epsilon\) for given samples.

property holomorphic: bool

Whether the state is holomorphic.

property imag_time: bool

Whether to use imaginary-time evolution.

property kazcmarz_mu: float

Whether to use the kazcmarz scheme.

solve(Obar: Array, Ebar: Array) → Array

Solve the equation \(\bar O \dot \theta = \bar \epsilon\) for given \(\bar O\) and \(\bar \epsilon\).

property state: Variational

Variational state to be optimized.

property vs_type: int

The vs_type of the state.